<scp>D</scp>r. <scp>E</scp>arl <scp>N</scp>. <scp>M</scp>eyer, in the lab, with a scalpel: A murder mystery as a biochemistry recruitment tool
Bibliographic record
Abstract
Increasing student participation in science is an ongoing challenge for many universities. In this active learning workshop, centered on inquiry and teamwork, we introduce high-school students to biochemistry and molecular biology techniques using a murder mystery activity. During this intensive 3 hr workshop, we engage students in a murder scenario entitled "The Case of the Silenced Scientist." A commercially available DNA fingerprinting kit was used as a basis to create a customized scenario whereby students collaborate with one another to solve a murder mystery. Through analysis of DNA samples taken from the crime scene and suspects, students can identify the murderer while developing technical, teamwork, and critical thinking skills. Emphasis is placed on teamwork by immersing students in the collaborative process of research inquiry. Though short in duration, this workshop aims to build student relationships to science through creativity and exploration. In this article, we describe the key customized applications of this workshop as a blueprint for science recruitment. We focus on the workshop facilitators' perceived learning impact on students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.383 | 0.132 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".